{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparisons using the BatchStudy class\n",
    "\n",
    "In this notebook, we will be going through the `BatchStudy` class and will be discussing how different models, experiments, chemistries, etc. can be compared with each other using the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing models\n",
    "We start by creating a simple script to compare `SPM`, `SPMe` and `DFN` model with the default parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install \"pybamm[plot,cite]\" -q    # install PyBaMM if it is not installed\n",
    "import pybamm\n",
    "\n",
    "# loading up 3 models to compare\n",
    "dfn = pybamm.lithium_ion.DFN()\n",
    "spm = pybamm.lithium_ion.SPM()\n",
    "spme = pybamm.lithium_ion.SPMe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `BatchStudy` class requires a dictionary of models, and all the default values for a given model are used if no additional parameter is passed in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e75fb30fed264dfa8e83893792677de3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ffdb8988b80>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = {\n",
    "    \"dfn\": dfn,\n",
    "    \"spm\": spm,\n",
    "    \"spme\": spme,\n",
    "}\n",
    "\n",
    "# creating a BatchStudy object\n",
    "batch_study = pybamm.BatchStudy(models=models)\n",
    "\n",
    "# solving and plotting the comparison\n",
    "batch_study.solve(t_eval=[0, 3600])\n",
    "batch_study.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`BatchStudy` by default requires equal number of items in all the dictionaries passed, which can be changed by setting the value of `permutations` to `True`. When set `True`, a cartesian product of all the available items is taken.\n",
    "\n",
    "For example, here we pass 3 models but only 1 parameter value, hence it is necessary to set `permutations` to `True`. Here, the given parameter value is used for all the provided models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25a776cd7e8d488eb68cc08b6e63c7b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='t', max=3567.8551657209678, step=35.67855165720968),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ffdcba8cfa0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# passing parameter_values as a dictionary\n",
    "parameter_values = {\"Chen2020\": pybamm.ParameterValues(\"Chen2020\")}\n",
    "\n",
    "# creating a BatchStudy object and solving the simulation\n",
    "batch_study = pybamm.BatchStudy(\n",
    "    models=models, parameter_values=parameter_values, permutations=True\n",
    ")\n",
    "batch_study.solve(t_eval=[0, 3600])\n",
    "batch_study.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing parameters\n",
    "\n",
    "`BatchStudy` can also be used to compare different things (like affect of changing a parameter's value) on a single model.\n",
    "\n",
    "In the following cell, we compare different values of `\"Curent function [A]\"` using the `Single Paritcle Model with electrolyte`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "420df8159595427da952bfd3f44846aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ffdccc5bd30>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = {\"spme\": spme}\n",
    "\n",
    "# populating a dictionary with 3 same parameter values\n",
    "parameter_values = {\n",
    "    \"Chen2020_1\": pybamm.ParameterValues(\"Chen2020\"),\n",
    "    \"Chen2020_2\": pybamm.ParameterValues(\"Chen2020\"),\n",
    "    \"Chen2020_3\": pybamm.ParameterValues(\"Chen2020\"),\n",
    "}\n",
    "\n",
    "# different values for \"Current function [A]\"\n",
    "current_values = [4.5, 4.75, 5]\n",
    "\n",
    "# changing the value of \"Current function [A]\" in all the parameter values present in the\n",
    "# parameter_values dictionary\n",
    "for _, v, current_value in zip(\n",
    "    parameter_values.keys(), parameter_values.values(), current_values, strict=False\n",
    "):\n",
    "    v[\"Current function [A]\"] = current_value\n",
    "\n",
    "# creating a BatchStudy object with permutations set to True to create a cartesian product\n",
    "batch_study = pybamm.BatchStudy(\n",
    "    models=model, parameter_values=parameter_values, permutations=True\n",
    ")\n",
    "batch_study.solve(t_eval=[0, 3600])\n",
    "\n",
    "# generating the required labels and plotting\n",
    "labels = [f\"Current function [A]: {current}\" for current in current_values]\n",
    "batch_study.plot(labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`BatchStudy` also includes a `create_gif` method which can be used to create a GIF of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using less number of images in the example\n",
    "# for a smoother GIF use more images\n",
    "batch_study.create_gif(number_of_images=5, duration=0.2, output_filename=\"batch.gif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displaying the GIF using markdown-\n",
    "\n",
    "![plot](https://user-images.githubusercontent.com/74055102/142717896-8152e816-71b1-47a7-b557-57cf6dbfc839.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using experiments\n",
    "\n",
    "Experiments can also be specified for comparisons, and they are also passed as a dictionary (a dictionary of `pybamm.Experiment`) in the `BatchStudy` class.\n",
    "\n",
    "In the next cell, we compare a single experiment, with a single model, but with a varied parameter value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "2022-07-26 16:44:49.209 - [NOTICE] callbacks.on_cycle_start(174): Cycle 7/10 (2.353 s elapsed) --------------------\n",
      "2022-07-26 16:44:49.210 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:49.335 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:49.350 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:49.416 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:49.430 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:49.533 - [NOTICE] callbacks.on_cycle_start(174): Cycle 8/10 (2.677 s elapsed) --------------------\n",
      "2022-07-26 16:44:49.534 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:49.658 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:49.673 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:49.740 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:49.755 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:49.851 - [NOTICE] callbacks.on_cycle_start(174): Cycle 9/10 (2.995 s elapsed) --------------------\n",
      "2022-07-26 16:44:49.852 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:49.977 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:49.992 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:50.065 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:50.078 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:50.176 - [NOTICE] callbacks.on_cycle_start(174): Cycle 10/10 (3.320 s elapsed) --------------------\n",
      "2022-07-26 16:44:50.176 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:50.307 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:50.324 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:50.420 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:50.445 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:50.542 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 3.320 s\n",
      "2022-07-26 16:44:51.360 - [NOTICE] callbacks.on_cycle_start(174): Cycle 1/10 (36.824 ms elapsed) --------------------\n",
      "2022-07-26 16:44:51.360 - [NOTICE] callbacks.on_step_start(182): Cycle 1/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:51.515 - [NOTICE] callbacks.on_step_start(182): Cycle 1/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:51.545 - [NOTICE] callbacks.on_step_start(182): Cycle 1/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:51.638 - [NOTICE] callbacks.on_step_start(182): Cycle 1/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:51.684 - [NOTICE] callbacks.on_step_start(182): Cycle 1/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:52.031 - [NOTICE] callbacks.on_cycle_start(174): Cycle 2/10 (707.971 ms elapsed) --------------------\n",
      "2022-07-26 16:44:52.031 - [NOTICE] callbacks.on_step_start(182): Cycle 2/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:52.156 - [NOTICE] callbacks.on_step_start(182): Cycle 2/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:52.171 - [NOTICE] callbacks.on_step_start(182): Cycle 2/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:52.238 - [NOTICE] callbacks.on_step_start(182): Cycle 2/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:52.251 - [NOTICE] callbacks.on_step_start(182): Cycle 2/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:52.355 - [NOTICE] callbacks.on_cycle_start(174): Cycle 3/10 (1.033 s elapsed) --------------------\n",
      "2022-07-26 16:44:52.356 - [NOTICE] callbacks.on_step_start(182): Cycle 3/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:52.480 - [NOTICE] callbacks.on_step_start(182): Cycle 3/10, step 2/5: Rest for 1 hour\n",
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      "2022-07-26 16:44:52.563 - [NOTICE] callbacks.on_step_start(182): Cycle 3/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:52.577 - [NOTICE] callbacks.on_step_start(182): Cycle 3/10, step 5/5: Rest for 1 hour\n",
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      "2022-07-26 16:44:52.676 - [NOTICE] callbacks.on_step_start(182): Cycle 4/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:52.800 - [NOTICE] callbacks.on_step_start(182): Cycle 4/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:52.815 - [NOTICE] callbacks.on_step_start(182): Cycle 4/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:52.881 - [NOTICE] callbacks.on_step_start(182): Cycle 4/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:52.895 - [NOTICE] callbacks.on_step_start(182): Cycle 4/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:52.989 - [NOTICE] callbacks.on_cycle_start(174): Cycle 5/10 (1.666 s elapsed) --------------------\n",
      "2022-07-26 16:44:52.990 - [NOTICE] callbacks.on_step_start(182): Cycle 5/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:53.114 - [NOTICE] callbacks.on_step_start(182): Cycle 5/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:53.129 - [NOTICE] callbacks.on_step_start(182): Cycle 5/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:53.196 - [NOTICE] callbacks.on_step_start(182): Cycle 5/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:53.210 - [NOTICE] callbacks.on_step_start(182): Cycle 5/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:53.308 - [NOTICE] callbacks.on_cycle_start(174): Cycle 6/10 (1.985 s elapsed) --------------------\n",
      "2022-07-26 16:44:53.308 - [NOTICE] callbacks.on_step_start(182): Cycle 6/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:53.432 - [NOTICE] callbacks.on_step_start(182): Cycle 6/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:53.447 - [NOTICE] callbacks.on_step_start(182): Cycle 6/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:53.517 - [NOTICE] callbacks.on_step_start(182): Cycle 6/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:53.531 - [NOTICE] callbacks.on_step_start(182): Cycle 6/10, step 5/5: Rest for 1 hour\n",
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      "2022-07-26 16:44:53.628 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:53.753 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:53.768 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:53.836 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:53.849 - [NOTICE] callbacks.on_step_start(182): Cycle 7/10, step 5/5: Rest for 1 hour\n",
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      "2022-07-26 16:44:53.945 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:54.072 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:54.086 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:54.160 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:54.173 - [NOTICE] callbacks.on_step_start(182): Cycle 8/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:54.269 - [NOTICE] callbacks.on_cycle_start(174): Cycle 9/10 (2.946 s elapsed) --------------------\n",
      "2022-07-26 16:44:54.269 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:54.392 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:54.413 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:54.484 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:54.498 - [NOTICE] callbacks.on_step_start(182): Cycle 9/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:54.603 - [NOTICE] callbacks.on_cycle_start(174): Cycle 10/10 (3.281 s elapsed) --------------------\n",
      "2022-07-26 16:44:54.604 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 1/5: Discharge at C/10 for 10 hours or until 3.3 V\n",
      "2022-07-26 16:44:54.731 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 2/5: Rest for 1 hour\n",
      "2022-07-26 16:44:54.747 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 3/5: Charge at 1 A until 4.1 V\n",
      "2022-07-26 16:44:54.816 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 4/5: Hold at 4.1 V until 50 mA\n",
      "2022-07-26 16:44:54.829 - [NOTICE] callbacks.on_step_start(182): Cycle 10/10, step 5/5: Rest for 1 hour\n",
      "2022-07-26 16:44:54.926 - [NOTICE] callbacks.on_experiment_end(222): Finish experiment simulation, took 3.281 s\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d8511cc965a4b84acbd4da910708cc2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='t', max=155.71556450330894, step=1.5571556450330895)…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ffdea372580>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pybamm.set_logging_level(\"NOTICE\")\n",
    "\n",
    "# using the cccv experiment with 10 cycles\n",
    "cccv = pybamm.Experiment(\n",
    "    [\n",
    "        (\n",
    "            \"Discharge at C/10 for 10 hours or until 3.3 V\",\n",
    "            \"Rest for 1 hour\",\n",
    "            \"Charge at 1 A until 4.1 V\",\n",
    "            \"Hold at 4.1 V until 50 mA\",\n",
    "            \"Rest for 1 hour\",\n",
    "        )\n",
    "    ]\n",
    "    * 10,\n",
    ")\n",
    "\n",
    "# creating the experiment dict\n",
    "experiment = {\"cccv\": cccv}\n",
    "\n",
    "# populating a dictionary with 3 same parameter values (Mohtat2020 chemistry)\n",
    "parameter_values = {\n",
    "    \"Mohtat2020_1\": pybamm.ParameterValues(\"Mohtat2020\"),\n",
    "    \"Mohtat2020_2\": pybamm.ParameterValues(\"Mohtat2020\"),\n",
    "    \"Mohtat2020_3\": pybamm.ParameterValues(\"Mohtat2020\"),\n",
    "}\n",
    "\n",
    "# different values for the parameter \"SEI open-circuit potential [V]\"\n",
    "sei_oc_v_values = [2.0e-4, 2.7e-4, 3.4e-4]\n",
    "\n",
    "# updating the value of \"SEI open-circuit potential [V]\" in all the dictionary items\n",
    "for _, v, sei_oc_v in zip(\n",
    "    parameter_values.keys(), parameter_values.values(), sei_oc_v_values, strict=False\n",
    "):\n",
    "    v.update(\n",
    "        {\"SEI open-circuit potential [V]\": sei_oc_v},\n",
    "    )\n",
    "\n",
    "# creating a Single Particle Model with \"electron-mitigation limited\" SEI\n",
    "model = {\"spm\": pybamm.lithium_ion.SPM({\"SEI\": \"electron-migration limited\"})}\n",
    "\n",
    "# creating a BatchStudy object with the given experimen, model and parameter_values\n",
    "batch_study = pybamm.BatchStudy(\n",
    "    models=model,\n",
    "    experiments=experiment,\n",
    "    parameter_values=parameter_values,\n",
    "    permutations=True,\n",
    ")\n",
    "\n",
    "# solving and plotting the result\n",
    "batch_study.solve(initial_soc=1)\n",
    "\n",
    "labels = [\n",
    "    f\"SEI open-circuit potential [V]: {inner_sei_oc_v}\"\n",
    "    for inner_sei_oc_v in sei_oc_v_values\n",
    "]\n",
    "batch_study.plot(labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The difference in the individual plots is not very well visible in the above slider plot, but we can access all the simulations created by `BatchStudy` (`batch_study.sims`) and pass it to `pybamm.plot_summary_variables` to plot the summary variables (more details on \"summary variables\" are available in the [`simulating-long-experiments`](./simulations_and_experiments/simulating-long-experiments.ipynb) notebook).\n",
    "\n",
    "## Comparing summary variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='Cycle number', ylabel='Capacity [A.h]'>,\n",
       "        <AxesSubplot:xlabel='Cycle number', ylabel='Loss of lithium inventory [%]'>,\n",
       "        <AxesSubplot:xlabel='Cycle number', ylabel='Loss of capacity to SEI [A.h]'>],\n",
       "       [<AxesSubplot:xlabel='Cycle number', ylabel='Loss of active material in negative electrode [%]'>,\n",
       "        <AxesSubplot:xlabel='Cycle number', ylabel='Loss of active material in positive electrode [%]'>,\n",
       "        <AxesSubplot:xlabel='Cycle number', ylabel='x_100'>],\n",
       "       [<AxesSubplot:xlabel='Cycle number', ylabel='x_0'>,\n",
       "        <AxesSubplot:xlabel='Cycle number', ylabel='y_100'>,\n",
       "        <AxesSubplot:xlabel='Cycle number', ylabel='y_0'>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pybamm.plot_summary_variables([sim.solution for sim in batch_study.sims])"
   ]
  },
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   "source": [
    "Other than the above examples, the `BatchStudy` class can be used to compare a lot of different configurations, like models with different SEIs or a model with reversible and irreversible lithium plating, etc."
   ]
  },
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   "source": [
    "## References\n",
    "\n",
    "The relevant papers for this notebook are:"
   ]
  },
  {
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     "text": [
      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
      "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
      "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
      "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
      "[5] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
      "[6] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
      "[7] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n",
      "[8] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
      "[9] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pybamm.print_citations()"
   ]
  }
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